library(xlsx);library(dplyr);library(lubridate)
options(scipen=999) #remove scientific notation
data <- read.csv2("taux_utilisation_actes.csv")
data_eat <- data[data$eat == "Oui",]
data_eat

Global variables

#window size
max.window.size <- 5 #size of the window = number of collaborative acts per window
max.window.size
## [1] 5
#dyad having used the emotion awareness tool (eat condition) for sharing their emotions 
dyads_eat <- c(
"D02",
"D05",
"D07",
"D09",
"D13",
"D16",
"D21",
"D23",
"D26",
"D27",
"D29")

#participants in eat condition
participants_eat <- c(
"D02P01",
"D02P02",
"D05P01",
"D05P02",
"D07P01",
"D07P02",
"D09P01",
"D09P02",
"D13P01",
"D13P02",
"D16P01",
"D16P02",
"D21P01",
"D21P02",
"D23P01",
"D23P02",
"D26P01",
"D26P02",
"D27P01",
"D27P02",
"D29P01",
"D29P02")

#participants in no-eat condition
participants_no_eat <- c(
"D04P01",
"D04P02",
"D06P01",
"D06P02",
"D08P01",
"D08P02",
"D10P01",
"D10P02",
"D12P01",
"D12P02",
"D15P01",
"D15P02",
"D20P01",
"D20P02",
"D24P01",
"D24P02")

#emotions displayed during the collaborative problem-solving
emotions <- c(
"interested",
"focused",
"delighted",
"satisfied",
"confident",
"amused",
"relieved",
"relaxed",
"confused",
"stressed",
"dissatisfied",
"annoyed",
"frustrated",
"grateful",
"disappointed",
"empathic",
"bored",
"surprised",
"puzzled",
"anxious",
"impatient")

#collaborative acts emitted by participants
collaborative.processes <- c(
"accept",
"agree",
"check.comprehension",
"check.reception",
"coordinate.teamwork",
"disagree",
"elicit.opinion",
"elicit.partner.information",
"elicit.proposition",
"elicit.recall",
"elicit.task.information",
"give.explanation",
"give.opinion.against",
"give.opinion.for",
"give.proposition",
"give.recall",
"give.self.information",
"give.task.information",
"incorporate",
"manage.task",
"other",
"outside.activity",
"relax.atmosphere",
"show.active.listening",
"show.reflection",
"show.hostility",
"show.solidarity",
"tool.discourse",
"use.social.convention")

RTU computation

Interested

#Find cases available (participants that have shared the emotion)
interested.cases <- c()

emotion = "interested"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    interested.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 2 correspondence(s)"
## [1] "For participant  D02P02  there is 3 correspondence(s)"
## [1] "For participant  D05P01  there is 0 correspondence(s)"
## [1] "For participant  D05P02  there is 1 correspondence(s)"
## [1] "For participant  D07P01  there is 1 correspondence(s)"
## [1] "For participant  D07P02  there is 2 correspondence(s)"
## [1] "For participant  D09P01  there is 3 correspondence(s)"
## [1] "For participant  D09P02  there is 4 correspondence(s)"
## [1] "For participant  D13P01  there is 1 correspondence(s)"
## [1] "For participant  D13P02  there is 3 correspondence(s)"
## [1] "For participant  D16P01  there is 2 correspondence(s)"
## [1] "For participant  D16P02  there is 3 correspondence(s)"
## [1] "For participant  D21P01  there is 2 correspondence(s)"
## [1] "For participant  D21P02  there is 1 correspondence(s)"
## [1] "For participant  D23P01  there is 2 correspondence(s)"
## [1] "For participant  D23P02  there is 2 correspondence(s)"
## [1] "For participant  D26P01  there is 1 correspondence(s)"
## [1] "For participant  D26P02  there is 0 correspondence(s)"
## [1] "For participant  D27P01  there is 0 correspondence(s)"
## [1] "For participant  D27P02  there is 1 correspondence(s)"
## [1] "For participant  D29P01  there is 2 correspondence(s)"
## [1] "For participant  D29P02  there is 3 correspondence(s)"
interested.available.cases <- interested.cases[interested.cases != 0]
interested.available.cases
## D02P01 D02P02 D05P02 D07P01 D07P02 D09P01 D09P02 D13P01 D13P02 D16P01 D16P02 
##      2      3      1      1      2      3      4      1      3      2      3 
## D21P01 D21P02 D23P01 D23P02 D26P01 D27P02 D29P01 D29P02 
##      2      1      2      2      1      1      2      3
length(interested.available.cases)
## [1] 19
names(interested.available.cases)
##  [1] "D02P01" "D02P02" "D05P02" "D07P01" "D07P02" "D09P01" "D09P02" "D13P01"
##  [9] "D13P02" "D16P01" "D16P02" "D21P01" "D21P02" "D23P01" "D23P02" "D26P01"
## [17] "D27P02" "D29P01" "D29P02"
#interested sample
participants_eat_interested <- c("D02P01","D07P01","D09P01","D13P01","D16P01","D21P01","D23P01","D26P01","D29P01", "D02P02","D05P02","D07P02","D09P02","D13P02","D16P02","D21P02","D23P02","D27P02","D29P02")

Compute the real-time use for the emotion interested

#select the subset of collaborative processes for a given participant. For the emotion interested, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
interested.table <- data.frame(n)
rownames(interested.table) <- collaborative.processes

for (p in participants_eat_interested)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    interested.collaborative.processes.table <- data.frame(m)
  
  interested.times <- data[which(data$participant == p & data$shared.emotion == "interested"),c("unix.time.video")]
  
   for (i in seq(1:length(interested.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 interested.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      interested.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  interested.collaborative.processes.table
  length(interested.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(interested.collaborative.processes.table[interested.collaborative.processes.table == i])
  }
  
  if (length(interested.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(interested.collaborative.processes.table) #number of emotion windows
      interested.table[,p] = x
    
     }
}

interested.table

Compute the real-time use when no emotion interested

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.interested.table <- data.frame(n)
rownames(non.interested.table) <- collaborative.processes

for (p in participants_eat_interested)
{
  interested.times <- data[which(data$participant == p & data$shared.emotion =="interested"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(interested.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 interested.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.interested.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}

non.interested.table = subset(non.interested.table, select = -c(D13P01,D05P02))
non.interested.table

focused

#Find cases available (participants that have shared the emotion)
focused.cases <- c()

emotion = "focused"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    focused.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 1 correspondence(s)"
## [1] "For participant  D02P02  there is 1 correspondence(s)"
## [1] "For participant  D05P01  there is 1 correspondence(s)"
## [1] "For participant  D05P02  there is 1 correspondence(s)"
## [1] "For participant  D07P01  there is 4 correspondence(s)"
## [1] "For participant  D07P02  there is 2 correspondence(s)"
## [1] "For participant  D09P01  there is 11 correspondence(s)"
## [1] "For participant  D09P02  there is 4 correspondence(s)"
## [1] "For participant  D13P01  there is 1 correspondence(s)"
## [1] "For participant  D13P02  there is 1 correspondence(s)"
## [1] "For participant  D16P01  there is 1 correspondence(s)"
## [1] "For participant  D16P02  there is 4 correspondence(s)"
## [1] "For participant  D21P01  there is 3 correspondence(s)"
## [1] "For participant  D21P02  there is 1 correspondence(s)"
## [1] "For participant  D23P01  there is 1 correspondence(s)"
## [1] "For participant  D23P02  there is 3 correspondence(s)"
## [1] "For participant  D26P01  there is 4 correspondence(s)"
## [1] "For participant  D26P02  there is 2 correspondence(s)"
## [1] "For participant  D27P01  there is 2 correspondence(s)"
## [1] "For participant  D27P02  there is 1 correspondence(s)"
## [1] "For participant  D29P01  there is 0 correspondence(s)"
## [1] "For participant  D29P02  there is 3 correspondence(s)"
focused.available.cases <- focused.cases[focused.cases != 0]
focused.available.cases
## D02P01 D02P02 D05P01 D05P02 D07P01 D07P02 D09P01 D09P02 D13P01 D13P02 D16P01 
##      1      1      1      1      4      2     11      4      1      1      1 
## D16P02 D21P01 D21P02 D23P01 D23P02 D26P01 D26P02 D27P01 D27P02 D29P02 
##      4      3      1      1      3      4      2      2      1      3
length(focused.available.cases)
## [1] 21
names(focused.available.cases)
##  [1] "D02P01" "D02P02" "D05P01" "D05P02" "D07P01" "D07P02" "D09P01" "D09P02"
##  [9] "D13P01" "D13P02" "D16P01" "D16P02" "D21P01" "D21P02" "D23P01" "D23P02"
## [17] "D26P01" "D26P02" "D27P01" "D27P02" "D29P02"
#focused sample
#focused sample
participants_eat_focused <- c("D02P01","D07P01","D07P01","D09P01","D13P01","D16P01","D21P01","D23P01","D26P01","D27P01","D02P02","D05P02","D07P02","D09P02","D13P02","D16P02","D21P02","D23P02","D26P02","D27P02","D29P02")

Compute the real-time use for the emotion focused

#select the subset of collaborative processes for a given participant. For the emotion focused, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
focused.table <- data.frame(n)
rownames(focused.table) <- collaborative.processes

for (p in participants_eat_focused)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    focused.collaborative.processes.table <- data.frame(m)
  
  focused.times <- data[which(data$participant == p & data$shared.emotion == "focused"),c("unix.time.video")]
  
   for (i in seq(1:length(focused.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 focused.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      focused.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  focused.collaborative.processes.table
  length(focused.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(focused.collaborative.processes.table[focused.collaborative.processes.table == i])
  }
  
  if (length(focused.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(focused.collaborative.processes.table) #number of emotion windows
      focused.table[,p] = x
    
     }
}

focused.table

Compute the real-time use when no emotion focused

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.focused.table <- data.frame(n)
rownames(non.focused.table) <- collaborative.processes

for (p in participants_eat_focused)
{
  focused.times <- data[which(data$participant == p & data$shared.emotion =="focused"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(focused.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 focused.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.focused.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}
non.focused.table = subset(non.focused.table, select = -c(D02P02))
non.focused.table

delighted

#Find cases available (participants that have shared the emotion)
delighted.cases <- c()

emotion = "delighted"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    delighted.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 1 correspondence(s)"
## [1] "For participant  D02P02  there is 0 correspondence(s)"
## [1] "For participant  D05P01  there is 2 correspondence(s)"
## [1] "For participant  D05P02  there is 0 correspondence(s)"
## [1] "For participant  D07P01  there is 1 correspondence(s)"
## [1] "For participant  D07P02  there is 1 correspondence(s)"
## [1] "For participant  D09P01  there is 2 correspondence(s)"
## [1] "For participant  D09P02  there is 1 correspondence(s)"
## [1] "For participant  D13P01  there is 1 correspondence(s)"
## [1] "For participant  D13P02  there is 3 correspondence(s)"
## [1] "For participant  D16P01  there is 0 correspondence(s)"
## [1] "For participant  D16P02  there is 0 correspondence(s)"
## [1] "For participant  D21P01  there is 0 correspondence(s)"
## [1] "For participant  D21P02  there is 1 correspondence(s)"
## [1] "For participant  D23P01  there is 0 correspondence(s)"
## [1] "For participant  D23P02  there is 0 correspondence(s)"
## [1] "For participant  D26P01  there is 1 correspondence(s)"
## [1] "For participant  D26P02  there is 1 correspondence(s)"
## [1] "For participant  D27P01  there is 1 correspondence(s)"
## [1] "For participant  D27P02  there is 2 correspondence(s)"
## [1] "For participant  D29P01  there is 1 correspondence(s)"
## [1] "For participant  D29P02  there is 1 correspondence(s)"
delighted.available.cases <- delighted.cases[delighted.cases != 0]
delighted.available.cases
## D02P01 D05P01 D07P01 D07P02 D09P01 D09P02 D13P01 D13P02 D21P02 D26P01 D26P02 
##      1      2      1      1      2      1      1      3      1      1      1 
## D27P01 D27P02 D29P01 D29P02 
##      1      2      1      1
length(delighted.available.cases)
## [1] 15
names(delighted.available.cases)
##  [1] "D02P01" "D05P01" "D07P01" "D07P02" "D09P01" "D09P02" "D13P01" "D13P02"
##  [9] "D21P02" "D26P01" "D26P02" "D27P01" "D27P02" "D29P01" "D29P02"
#delighted sample
participants_eat_delighted <- c("D02P01","D05P01","D09P01","D26P01","D27P01","D29P01","D09P02","D13P02","D21P02","D27P02","D29P02")

Compute the real-time use for the emotion delighted

#select the subset of collaborative processes for a given participant. For the emotion delighted, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
delighted.table <- data.frame(n)
rownames(delighted.table) <- collaborative.processes

for (p in participants_eat_delighted)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    delighted.collaborative.processes.table <- data.frame(m)
  
  delighted.times <- data[which(data$participant == p & data$shared.emotion == "delighted"),c("unix.time.video")]
  
   for (i in seq(1:length(delighted.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 delighted.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      delighted.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  delighted.collaborative.processes.table
  length(delighted.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(delighted.collaborative.processes.table[delighted.collaborative.processes.table == i])
  }
  
  if (length(delighted.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(delighted.collaborative.processes.table) #number of emotion windows
      delighted.table[,p] = x
    
     }
}

delighted.table

Compute the real-time use when no emotion delighted

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.delighted.table <- data.frame(n)
rownames(non.delighted.table) <- collaborative.processes

for (p in participants_eat_delighted)
{
  delighted.times <- data[which(data$participant == p & data$shared.emotion =="delighted"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(delighted.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 delighted.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.delighted.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}

non.delighted.table

satisfied

#Find cases available (participants that have shared the emotion)
satisfied.cases <- c()

emotion = "satisfied"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    satisfied.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 2 correspondence(s)"
## [1] "For participant  D02P02  there is 2 correspondence(s)"
## [1] "For participant  D05P01  there is 2 correspondence(s)"
## [1] "For participant  D05P02  there is 0 correspondence(s)"
## [1] "For participant  D07P01  there is 0 correspondence(s)"
## [1] "For participant  D07P02  there is 0 correspondence(s)"
## [1] "For participant  D09P01  there is 2 correspondence(s)"
## [1] "For participant  D09P02  there is 2 correspondence(s)"
## [1] "For participant  D13P01  there is 1 correspondence(s)"
## [1] "For participant  D13P02  there is 4 correspondence(s)"
## [1] "For participant  D16P01  there is 4 correspondence(s)"
## [1] "For participant  D16P02  there is 1 correspondence(s)"
## [1] "For participant  D21P01  there is 2 correspondence(s)"
## [1] "For participant  D21P02  there is 3 correspondence(s)"
## [1] "For participant  D23P01  there is 2 correspondence(s)"
## [1] "For participant  D23P02  there is 0 correspondence(s)"
## [1] "For participant  D26P01  there is 2 correspondence(s)"
## [1] "For participant  D26P02  there is 5 correspondence(s)"
## [1] "For participant  D27P01  there is 1 correspondence(s)"
## [1] "For participant  D27P02  there is 2 correspondence(s)"
## [1] "For participant  D29P01  there is 1 correspondence(s)"
## [1] "For participant  D29P02  there is 2 correspondence(s)"
satisfied.available.cases <- satisfied.cases[satisfied.cases != 0]
satisfied.available.cases
## D02P01 D02P02 D05P01 D09P01 D09P02 D13P01 D13P02 D16P01 D16P02 D21P01 D21P02 
##      2      2      2      2      2      1      4      4      1      2      3 
## D23P01 D26P01 D26P02 D27P01 D27P02 D29P01 D29P02 
##      2      2      5      1      2      1      2
length(satisfied.available.cases)
## [1] 18
names(satisfied.available.cases)
##  [1] "D02P01" "D02P02" "D05P01" "D09P01" "D09P02" "D13P01" "D13P02" "D16P01"
##  [9] "D16P02" "D21P01" "D21P02" "D23P01" "D26P01" "D26P02" "D27P01" "D27P02"
## [17] "D29P01" "D29P02"
#satisfied sample
participants_eat_satisfied <- c("D02P01","D05P01","D09P01","D16P01","D21P01","D23P01","D26P01","D29P01","D02P02","D09P02","D13P02","D16P02","D21P02","D26P02","D27P02","D29P02")

Compute the real-time use for the emotion satisfied

#select the subset of collaborative processes for a given participant. For the emotion satisfied, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
satisfied.table <- data.frame(n)
rownames(satisfied.table) <- collaborative.processes

for (p in participants_eat_satisfied)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    satisfied.collaborative.processes.table <- data.frame(m)
  
  satisfied.times <- data[which(data$participant == p & data$shared.emotion == "satisfied"),c("unix.time.video")]
  
   for (i in seq(1:length(satisfied.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 satisfied.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      satisfied.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  satisfied.collaborative.processes.table
  length(satisfied.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(satisfied.collaborative.processes.table[satisfied.collaborative.processes.table == i])
  }
  
  if (length(satisfied.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(satisfied.collaborative.processes.table) #number of emotion windows
      satisfied.table[,p] = x
    
     }
}

satisfied.table

Compute the real-time use when no emotion satisfied

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.satisfied.table <- data.frame(n)
rownames(non.satisfied.table) <- collaborative.processes

for (p in participants_eat_satisfied)
{
  satisfied.times <- data[which(data$participant == p & data$shared.emotion =="satisfied"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(satisfied.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 satisfied.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.satisfied.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}

non.satisfied.table

amused

#Find cases available (participants that have shared the emotion)
amused.cases <- c()

emotion = "amused"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    amused.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 1 correspondence(s)"
## [1] "For participant  D02P02  there is 0 correspondence(s)"
## [1] "For participant  D05P01  there is 1 correspondence(s)"
## [1] "For participant  D05P02  there is 0 correspondence(s)"
## [1] "For participant  D07P01  there is 3 correspondence(s)"
## [1] "For participant  D07P02  there is 1 correspondence(s)"
## [1] "For participant  D09P01  there is 5 correspondence(s)"
## [1] "For participant  D09P02  there is 3 correspondence(s)"
## [1] "For participant  D13P01  there is 2 correspondence(s)"
## [1] "For participant  D13P02  there is 1 correspondence(s)"
## [1] "For participant  D16P01  there is 1 correspondence(s)"
## [1] "For participant  D16P02  there is 0 correspondence(s)"
## [1] "For participant  D21P01  there is 2 correspondence(s)"
## [1] "For participant  D21P02  there is 3 correspondence(s)"
## [1] "For participant  D23P01  there is 1 correspondence(s)"
## [1] "For participant  D23P02  there is 1 correspondence(s)"
## [1] "For participant  D26P01  there is 2 correspondence(s)"
## [1] "For participant  D26P02  there is 2 correspondence(s)"
## [1] "For participant  D27P01  there is 2 correspondence(s)"
## [1] "For participant  D27P02  there is 1 correspondence(s)"
## [1] "For participant  D29P01  there is 2 correspondence(s)"
## [1] "For participant  D29P02  there is 0 correspondence(s)"
amused.available.cases <- amused.cases[amused.cases != 0]
amused.available.cases
## D02P01 D05P01 D07P01 D07P02 D09P01 D09P02 D13P01 D13P02 D16P01 D21P01 D21P02 
##      1      1      3      1      5      3      2      1      1      2      3 
## D23P01 D23P02 D26P01 D26P02 D27P01 D27P02 D29P01 
##      1      1      2      2      2      1      2
length(amused.available.cases)
## [1] 18
names(amused.available.cases)
##  [1] "D02P01" "D05P01" "D07P01" "D07P02" "D09P01" "D09P02" "D13P01" "D13P02"
##  [9] "D16P01" "D21P01" "D21P02" "D23P01" "D23P02" "D26P01" "D26P02" "D27P01"
## [17] "D27P02" "D29P01"
#amused sample
participants_eat_amused <- c("D02P01","D05P01","D07P01","D09P01","D13P01","D16P01","D21P01","D23P01","D26P01","D27P01","D29P01","D07P02","D09P02","D13P02","D21P02","D23P02","D26P02","D27P02")

Compute the real-time use for the emotion amused

#select the subset of collaborative processes for a given participant. For the emotion amused, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
amused.table <- data.frame(n)
rownames(amused.table) <- collaborative.processes

for (p in participants_eat_amused)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    amused.collaborative.processes.table <- data.frame(m)
  
  amused.times <- data[which(data$participant == p & data$shared.emotion == "amused"),c("unix.time.video")]
  
   for (i in seq(1:length(amused.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 amused.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      amused.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  amused.collaborative.processes.table
  length(amused.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(amused.collaborative.processes.table[amused.collaborative.processes.table == i])
  }
  
  if (length(amused.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(amused.collaborative.processes.table) #number of emotion windows
      amused.table[,p] = x
    
     }
}

amused.table

Compute the real-time use when no emotion amused

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.amused.table <- data.frame(n)
rownames(non.amused.table) <- collaborative.processes

for (p in participants_eat_amused)
{
  amused.times <- data[which(data$participant == p & data$shared.emotion =="amused"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(amused.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 amused.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.amused.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}

non.amused.table

relaxed

#Find cases available (participants that have shared the emotion)
relaxed.cases <- c()

emotion = "relaxed"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    relaxed.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 0 correspondence(s)"
## [1] "For participant  D02P02  there is 0 correspondence(s)"
## [1] "For participant  D05P01  there is 1 correspondence(s)"
## [1] "For participant  D05P02  there is 2 correspondence(s)"
## [1] "For participant  D07P01  there is 1 correspondence(s)"
## [1] "For participant  D07P02  there is 0 correspondence(s)"
## [1] "For participant  D09P01  there is 1 correspondence(s)"
## [1] "For participant  D09P02  there is 2 correspondence(s)"
## [1] "For participant  D13P01  there is 0 correspondence(s)"
## [1] "For participant  D13P02  there is 1 correspondence(s)"
## [1] "For participant  D16P01  there is 0 correspondence(s)"
## [1] "For participant  D16P02  there is 1 correspondence(s)"
## [1] "For participant  D21P01  there is 2 correspondence(s)"
## [1] "For participant  D21P02  there is 2 correspondence(s)"
## [1] "For participant  D23P01  there is 1 correspondence(s)"
## [1] "For participant  D23P02  there is 0 correspondence(s)"
## [1] "For participant  D26P01  there is 1 correspondence(s)"
## [1] "For participant  D26P02  there is 2 correspondence(s)"
## [1] "For participant  D27P01  there is 1 correspondence(s)"
## [1] "For participant  D27P02  there is 1 correspondence(s)"
## [1] "For participant  D29P01  there is 0 correspondence(s)"
## [1] "For participant  D29P02  there is 0 correspondence(s)"
relaxed.available.cases <- relaxed.cases[relaxed.cases != 0]
relaxed.available.cases
## D05P01 D05P02 D07P01 D09P01 D09P02 D13P02 D16P02 D21P01 D21P02 D23P01 D26P01 
##      1      2      1      1      2      1      1      2      2      1      1 
## D26P02 D27P01 D27P02 
##      2      1      1
length(relaxed.available.cases)
## [1] 14
names(relaxed.available.cases)
##  [1] "D05P01" "D05P02" "D07P01" "D09P01" "D09P02" "D13P02" "D16P02" "D21P01"
##  [9] "D21P02" "D23P01" "D26P01" "D26P02" "D27P01" "D27P02"
#relaxed sample
participants_eat_relaxed <- c("D05P01","D07P01","D09P01","D21P01","D23P01","D27P01","D05P02","D09P02","D16P02","D21P02","D26P02","D27P02")

Compute the real-time use for the emotion relaxed

#select the subset of collaborative processes for a given participant. For the emotion relaxed, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
relaxed.table <- data.frame(n)
rownames(relaxed.table) <- collaborative.processes

for (p in participants_eat_relaxed)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    relaxed.collaborative.processes.table <- data.frame(m)
  
  relaxed.times <- data[which(data$participant == p & data$shared.emotion == "relaxed"),c("unix.time.video")]
  
   for (i in seq(1:length(relaxed.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 relaxed.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      relaxed.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  relaxed.collaborative.processes.table
  length(relaxed.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(relaxed.collaborative.processes.table[relaxed.collaborative.processes.table == i])
  }
  
  if (length(relaxed.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(relaxed.collaborative.processes.table) #number of emotion windows
      relaxed.table[,p] = x
    
     }
}

relaxed.table

Compute the real-time use when no emotion relaxed

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.relaxed.table <- data.frame(n)
rownames(non.relaxed.table) <- collaborative.processes

for (p in participants_eat_relaxed)
{
  relaxed.times <- data[which(data$participant == p & data$shared.emotion =="relaxed"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(relaxed.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 relaxed.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.relaxed.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}

non.relaxed.table = subset(non.relaxed.table, select = -c(D07P01,D23P01, D16P02))
non.relaxed.table

confident

#Find cases available (participants that have shared the emotion)
confident.cases <- c()

emotion = "confident"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    confident.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 0 correspondence(s)"
## [1] "For participant  D02P02  there is 2 correspondence(s)"
## [1] "For participant  D05P01  there is 0 correspondence(s)"
## [1] "For participant  D05P02  there is 1 correspondence(s)"
## [1] "For participant  D07P01  there is 0 correspondence(s)"
## [1] "For participant  D07P02  there is 0 correspondence(s)"
## [1] "For participant  D09P01  there is 1 correspondence(s)"
## [1] "For participant  D09P02  there is 0 correspondence(s)"
## [1] "For participant  D13P01  there is 0 correspondence(s)"
## [1] "For participant  D13P02  there is 0 correspondence(s)"
## [1] "For participant  D16P01  there is 4 correspondence(s)"
## [1] "For participant  D16P02  there is 1 correspondence(s)"
## [1] "For participant  D21P01  there is 0 correspondence(s)"
## [1] "For participant  D21P02  there is 0 correspondence(s)"
## [1] "For participant  D23P01  there is 0 correspondence(s)"
## [1] "For participant  D23P02  there is 0 correspondence(s)"
## [1] "For participant  D26P01  there is 3 correspondence(s)"
## [1] "For participant  D26P02  there is 2 correspondence(s)"
## [1] "For participant  D27P01  there is 1 correspondence(s)"
## [1] "For participant  D27P02  there is 0 correspondence(s)"
## [1] "For participant  D29P01  there is 1 correspondence(s)"
## [1] "For participant  D29P02  there is 0 correspondence(s)"
confident.available.cases <- confident.cases[confident.cases != 0]
confident.available.cases
## D02P02 D05P02 D09P01 D16P01 D16P02 D26P01 D26P02 D27P01 D29P01 
##      2      1      1      4      1      3      2      1      1
length(confident.available.cases)
## [1] 9
names(confident.available.cases)
## [1] "D02P02" "D05P02" "D09P01" "D16P01" "D16P02" "D26P01" "D26P02" "D27P01"
## [9] "D29P01"
#confident sample
participants_eat_confident <- c("D09P01","D16P01","D26P01","D27P01","D29P01","D02P02","D05P02","D16P02","D26P02")

Compute the real-time use for the emotion confident

#select the subset of collaborative processes for a given participant. For the emotion confident, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
confident.table <- data.frame(n)
rownames(confident.table) <- collaborative.processes

for (p in participants_eat_confident)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    confident.collaborative.processes.table <- data.frame(m)
  
  confident.times <- data[which(data$participant == p & data$shared.emotion == "confident"),c("unix.time.video")]
  
   for (i in seq(1:length(confident.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 confident.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      confident.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  confident.collaborative.processes.table
  length(confident.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(confident.collaborative.processes.table[confident.collaborative.processes.table == i])
  }
  
  if (length(confident.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(confident.collaborative.processes.table) #number of emotion windows
      confident.table[,p] = x
    
     }
}

confident.table

Compute the real-time use when no emotion confident

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.confident.table <- data.frame(n)
rownames(non.confident.table) <- collaborative.processes

for (p in participants_eat_confident)
{
  confident.times <- data[which(data$participant == p & data$shared.emotion =="confident"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(confident.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 confident.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.confident.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}

non.confident.table

confused

#Find cases available (participants that have shared the emotion)
confused.cases <- c()

emotion = "confused"
for (participant in participants_eat) {
    print(paste("For participant ", participant," there is", length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")]),"correspondence(s)"))
    confused.cases[participant] <- length(data[which(data$participant == participant & data$shared.emotion == emotion),c("shared.emotion")])
}
## [1] "For participant  D02P01  there is 0 correspondence(s)"
## [1] "For participant  D02P02  there is 0 correspondence(s)"
## [1] "For participant  D05P01  there is 1 correspondence(s)"
## [1] "For participant  D05P02  there is 0 correspondence(s)"
## [1] "For participant  D07P01  there is 1 correspondence(s)"
## [1] "For participant  D07P02  there is 1 correspondence(s)"
## [1] "For participant  D09P01  there is 0 correspondence(s)"
## [1] "For participant  D09P02  there is 0 correspondence(s)"
## [1] "For participant  D13P01  there is 2 correspondence(s)"
## [1] "For participant  D13P02  there is 0 correspondence(s)"
## [1] "For participant  D16P01  there is 0 correspondence(s)"
## [1] "For participant  D16P02  there is 0 correspondence(s)"
## [1] "For participant  D21P01  there is 0 correspondence(s)"
## [1] "For participant  D21P02  there is 0 correspondence(s)"
## [1] "For participant  D23P01  there is 0 correspondence(s)"
## [1] "For participant  D23P02  there is 0 correspondence(s)"
## [1] "For participant  D26P01  there is 0 correspondence(s)"
## [1] "For participant  D26P02  there is 2 correspondence(s)"
## [1] "For participant  D27P01  there is 0 correspondence(s)"
## [1] "For participant  D27P02  there is 0 correspondence(s)"
## [1] "For participant  D29P01  there is 2 correspondence(s)"
## [1] "For participant  D29P02  there is 0 correspondence(s)"
confused.available.cases <- confused.cases[confused.cases != 0]
confused.available.cases
## D05P01 D07P01 D07P02 D13P01 D26P02 D29P01 
##      1      1      1      2      2      2
length(confused.available.cases)
## [1] 6
names(confused.available.cases)
## [1] "D05P01" "D07P01" "D07P02" "D13P01" "D26P02" "D29P01"
#confused sample
participants_eat_confused <- c("D05P01","D07P01","D13P01","D29P01","D07P02","D26P02")

Compute the real-time use for the emotion confused

#select the subset of collaborative processes for a given participant. For the emotion confused, the (max.windows.size) collaborative processes of the other's partner from the same dyad are selected


n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
confused.table <- data.frame(n)
rownames(confused.table) <- collaborative.processes

for (p in participants_eat_confused)

{
  m <- matrix(data=NA,nrow=max.window.size,ncol=0)
    confused.collaborative.processes.table <- data.frame(m)
  
  confused.times <- data[which(data$participant == p & data$shared.emotion == "confused"),c("unix.time.video")]
  
   for (i in seq(1:length(confused.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 confused.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      confused.collaborative.processes.table[,paste0("occurence",i)] <-
      selection[length(selection):(length(selection)-(max.window.size-1))]
   }
  
  confused.collaborative.processes.table
  length(confused.collaborative.processes.table)
  
  #compute the real-time use for every collaborative process
  x <- c()
  for (i in collaborative.processes)
  {
    x[i] <- length(confused.collaborative.processes.table[confused.collaborative.processes.table == i])
  }
  
  if (length(confused.collaborative.processes.table) > 0) 
      
      {
    
      x <- x/length(confused.collaborative.processes.table) #number of emotion windows
      confused.table[,p] = x
    
     }
}

confused.table

Compute the real-time use when no emotion confused

#Select a subset of the data not including the emotion windows

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
non.confused.table <- data.frame(n)
rownames(non.confused.table) <- collaborative.processes

for (p in participants_eat_confused)
{
  confused.times <- data[which(data$participant == p & data$shared.emotion =="confused"),c("unix.time.video")]
  to.be.dropped <- vector()
  
  for (i in seq(1:length(confused.times)))
    {
      selection <- data[which(data$participant == p & data$unix.time.video <                                                 confused.times[i]),c("collaborative.processes")]
      
     if (length(selection) < max.window.size)
     {
       next
     }
      
      to.be.dropped <- c(to.be.dropped,selection[length(selection):(length(selection)-(max.window.size-1))])
    }
  
    #remove NA when max.window.size goes out of bounds because the end of collaborative acts for that participant is reached
  to.be.dropped <- to.be.dropped[!is.na(to.be.dropped)]
  data.kept <- data[data$participant == p,][-to.be.dropped,] #data.kept is the data without emotion windows
  
  #Keep a number of lines that is a multiple of max.windows.size = number of no-emotion windows
  n <- nrow(data.kept)
  data.kept <- data.kept[c(1:(n-(n%%max.window.size))),]
  
  length(data.kept$participant)
  
  results <- c()
  for (process in collaborative.processes)
    {
    results <- c(results, sum(data.kept$collaborative.processes==process))
      }
  
  non.confused.table[,p] <- results/(length(data.kept$collaborative.processes)%/%max.window.size)
  results <- c()
  
}

non.confused.table

Permuation tests

number_of_permutations = 9999

n <- matrix(data=NA,nrow=length(collaborative.processes),ncol=0)
emotions.results.permutation.correction <- data.frame(n)
rownames(emotions.results.permutation.correction) <- collaborative.processes

interested

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(interested.table[process,])) - mean(as.numeric(non.interested.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(interested.table[process,]),as.numeric(non.interested.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(interested.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(interested.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.interested.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(interested.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.interested.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(interested.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$interested_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$interested_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$interested_mean.emotion <- mean.emotion
emotions.results.permutation.correction$interested_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$interested_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("interested_p.value"), drop=FALSE]

focused

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(focused.table[process,])) - mean(as.numeric(non.focused.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(focused.table[process,]),as.numeric(non.focused.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(focused.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(focused.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.focused.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(focused.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.focused.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(focused.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$focused_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$focused_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$focused_mean.emotion <- mean.emotion
emotions.results.permutation.correction$focused_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$focused_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("focused_p.value"), drop=FALSE]

delighted

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(delighted.table[process,])) - mean(as.numeric(non.delighted.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(delighted.table[process,]),as.numeric(non.delighted.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(delighted.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(delighted.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.delighted.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(delighted.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.delighted.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(delighted.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$delighted_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$delighted_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$delighted_mean.emotion <- mean.emotion
emotions.results.permutation.correction$delighted_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$delighted_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("delighted_p.value"), drop=FALSE]

satisfied

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(satisfied.table[process,])) - mean(as.numeric(non.satisfied.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(satisfied.table[process,]),as.numeric(non.satisfied.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(satisfied.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(satisfied.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.satisfied.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(satisfied.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.satisfied.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(satisfied.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$satisfied_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$satisfied_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$satisfied_mean.emotion <- mean.emotion
emotions.results.permutation.correction$satisfied_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$satisfied_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("satisfied_p.value"), drop=FALSE]

amused

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(amused.table[process,])) - mean(as.numeric(non.amused.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(amused.table[process,]),as.numeric(non.amused.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(amused.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(amused.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.amused.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(amused.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.amused.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(amused.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$amused_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$amused_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$amused_mean.emotion <- mean.emotion
emotions.results.permutation.correction$amused_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$amused_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("amused_p.value"), drop=FALSE]

relaxed

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(relaxed.table[process,])) - mean(as.numeric(non.relaxed.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(relaxed.table[process,]),as.numeric(non.relaxed.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(relaxed.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(relaxed.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.relaxed.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(relaxed.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.relaxed.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(relaxed.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$relaxed_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$relaxed_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$relaxed_mean.emotion <- mean.emotion
emotions.results.permutation.correction$relaxed_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$relaxed_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("relaxed_p.value"), drop=FALSE]

confident

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(confident.table[process,])) - mean(as.numeric(non.confident.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(confident.table[process,]),as.numeric(non.confident.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(confident.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(confident.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.confident.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(confident.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.confident.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(confident.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$confident_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$confident_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$confident_mean.emotion <- mean.emotion
emotions.results.permutation.correction$confident_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$confident_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("confident_p.value"), drop=FALSE]

confused

results <- c()
mean.no.emotion <- c()
mean.emotion <- c()
sd.no.emotion <- c()
sd.emotion <- c()

for (process in collaborative.processes) {

  observed.mean.difference <- mean(as.numeric(confused.table[process,])) - mean(as.numeric(non.confused.table[process,]))
  observed.mean.difference
  
  random.mean.differences <- c(observed.mean.difference)
  
  whole.sample <- c(as.numeric(confused.table[process,]),as.numeric(non.confused.table[process,])) #put together the two samples
  
  for (i in 1:number_of_permutations) {
    
    random.sample <- sample(whole.sample) #randomize the sample
    random.treatment.sample <- random.sample[1:length(as.numeric(confused.table[process,]))] #select new treatment sample
    random.control.sample <- random.sample[(1+length(as.numeric(confused.table[process,]))):length(random.sample)] #select new control sample
    random.mean.differences <- c(random.mean.differences,mean(random.treatment.sample)-mean(random.control.sample))
  
  }
  
    results <- c(results, sum(abs(random.mean.differences) >= abs(observed.mean.difference)) / length(random.mean.differences))
    mean.no.emotion <- c(mean.no.emotion,mean(as.numeric(non.confused.table[process,])))
    mean.emotion <- c(mean.emotion,mean(as.numeric(confused.table[process,])))
    sd.no.emotion <- c(sd.no.emotion,sd(as.numeric(non.confused.table[process,])))
    sd.emotion <- c(sd.emotion,sd(as.numeric(confused.table[process,])))
    
    #graphs
    hist(random.mean.differences, main = paste0("Sampling distribution ",process), xlab = "Mean difference", prob = F, col = "darkred")
    abline(v = observed.mean.difference, col = "blue", lwd = 2)
    
}

emotions.results.permutation.correction$confused_p.value <- p.adjust(results, method="BH")
emotions.results.permutation.correction$confused_mean.no.emotion <- mean.no.emotion
emotions.results.permutation.correction$confused_mean.emotion <- mean.emotion
emotions.results.permutation.correction$confused_sd.no.emotion <- sd.no.emotion
emotions.results.permutation.correction$confused_sd.emotion <- sd.emotion

emotions.results.permutation.correction[,c("confused_p.value"), drop=FALSE]

Export

#Exporting results in .xlsx file

write.xlsx(emotions.results.permutation.correction, paste0("results_emitter",toString(max.window.size),".xlsx"))